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Micro-blog Friend Recommendation Algorithms Based on Content and Social Relationship

  • Liangbin Yang
  • Binyang Li
  • Xinli Zhou
  • Yanmei Kang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 422)

Abstract

First, this paper researches the micro-blog information push, which leads to the concept of user’s friends, expounds the reason and meaning of friends recommendation algorithm, and introduces its current research situation, the paper has made the detailed introduction and analysis of existing algorithms and made a comprehensive comparison of the advantages and disadvantages of them. Then we make a recommendation of the micro-blog friend recommendation algorithms, which has two broad categories and three types: the recommendation algorithm based on content, the topology recommendation algorithm based on social relations and the filtering recommendation algorithm. Through the analysis of existing micro-blog friends recommendation algorithm, we represent the process of the algorithm and emphatically elaborated the implementation process, and finally we work out the Reasonable weighting of the three recommendation algorithm, get a sequence of recommended as a result, improved the algorithms, and reached a more comprehensive recommendation method. The improved algorithm could be a more effective way of potentially friends recommended for users.

Keywords

Micro-blog Information push Social relationship Friend recommendation Algorithm 

Notes

Acknowledgements

Supported by “the Fundamental Research Funds for the Central Universities” and “National Natural Science Foundation of China”, Project No. 3262015T20, 3262016T31, 3262015T70, 3262014T75, 61502115. Project Leader: Liangbin YANG; Binyang LI.

References

  1. 1.
    Wang Binghui. The research of the potential Friends’ recommend Algorithm in social network [D]. YUNNAN UNIVERSITY, 2013.Google Scholar
  2. 2.
    Chen J, Geyer W, Dugan C, et al. Make new friends, but keep the old: recommending people on social networking sites [C]. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2009: 201–210.Google Scholar
  3. 3.
    Tao Jun, Zhang Ning. Classification of collaborative filtering recommendation algorithm based on user interest [J]. COMPUTER APPLYMENT, 2011, 5(11):55–59.Google Scholar
  4. 4.
    Xie Yuan, Feng Lifang. Build your “social graph” [J]. Successful Marketing, 2010, 12(12):37–38.Google Scholar
  5. 5.
    Massa P, Bhattacharjee B. Using trust in recommender systems: an experimental analysis [M]. Trust Management. Springer Berlin Heidelberg, 2004: 221–235.Google Scholar
  6. 6.
    Lo S, Lin C. Wmr-a graph-based algorithm for friend recommendation [C]. Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence. IEEE Computer Society, 2006:121–128.Google Scholar
  7. 7.
    Chin A. Finding cohesive subgroups and relevant members in the Nokia friend view mobile social network [C]. Computational Science and Engineering, 2009. CSE’09. International Conference on. IEEE, 2009, 4: 278–283.Google Scholar
  8. 8.
    Shen D, Sun J T, Yang Q, et al. Latent friend mining from blog data [C]. Data Mining, 2006. ICDM’06. Sixth International Conference on. IEEE, 2006: 552–561.Google Scholar
  9. 9.
    Zheng Y, Chen Y, Xie X, et al. GeoLife2.0: a location-based social networking service [C]. Mobile Data Management: Systems, Services and Middleware, 2009. MDM’09. Tenth International Conference on. IEEE, 2009: 357–358.Google Scholar
  10. 10.
    Bacon K, Dewan P. Towards automatic recommendation of friend lists [C]. Collaborative Computing: Networking, Applications and Worksharing, 2009. CollaborateCom 2009. 5th International Conference on. IEEE, 2009:1–5.Google Scholar
  11. 11.
    Wu Z, Jiang S, Huang Q. Friend recommendation according to appearances on photos [C]. Proceedings of the 17th ACM international conference on Multimedia. ACM, 2009:987–988.Google Scholar
  12. 12.
    Yu Haiqun, Liu Wanjun, Qiu Yunfei. The secondary contacts of social network recommend which is based on the user preference topic [J]. Computer application, 2012, 32(5): 1366–1370.Google Scholar
  13. 13.
    Niu Qingpeng. The study of potential blog friend technology [D]. Shenyang: Northeastern University, 2009.Google Scholar
  14. 14.
    Shi Lingfeng. Query Algorithm Research and Application Based on the relationship of FIG social networking friends [D]. Nanjing: Nanjing University of Science and Technology, 2012.Google Scholar
  15. 15.
    Zhao Wenbing, Zhu Qinghua, Wu Kewen, etc. Micro-blog user characteristics and motivations analysis [J]. Library and Information Technology, 2011, 2.Google Scholar
  16. 16.
    Gou L, You F, Guo J, et al. SFViz: interest-based friends exploration and recommendation in social networks [C]. Proceedings of the 2011 Visual Information Communication-International Symposium. ACM, 2011: 15.Google Scholar
  17. 17.
    Xie X. Potential friend recommendation in online social network [C]. Green Computing and Communications (GreenCom), 2010 IEEE/ACM Int’l Conference on & Int’l Conference on Cyber, Physical and Social Computing (CPSCom). IEEE, 2010: 831–835.Google Scholar
  18. 18.
    Hannon J, Bennett M, Smyth B. Recommending twitter users to follow using content and collaborative filtering approaches [C]. Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010: 199–206.Google Scholar
  19. 19.
    Yu Yan, Qiu Guanghua, Chen Aiping. Recommendation algorithm based on online social network friends mixed graphs [J]. Library and Information Technology, 2011 (11): 54–59.Google Scholar
  20. 20.
    Java A, Song X, Finin T, et al. Why we twitter: understanding micro blogging usage and communities [C]. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis. ACM, 2007: 56–65.Google Scholar
  21. 21.
    Jia-jia Zheng. Social network of friends and Implementation Mechanism recommendation based on FIG. Sort [D]. Zhejiang University, 2011.Google Scholar
  22. 22.
    Armentano, M.G., D.L. Godoy, A.A. Amandi. A topology-based approach for followees recommendation in Twitter, in Workshop chairs.Google Scholar
  23. 23.
    Wu Yanqing. microblog friends’ recommendation which is based on heterogeneous data [D]. Zhejiang University, 2013.Google Scholar
  24. 24.
    Yang Honglei. Recommendation algorithm based on content and social filtering Friends [D]. Inner Mongolia University of Science and Technology, 2013.Google Scholar
  25. 25.
    Geyer W, Dugan C, Millen D R, et al. Recommending topics for self-descriptions in online user profiles [C]. Proceedings of the 2008 ACM conference on Recommender systems. ACM, 2008: 59–66.Google Scholar
  26. 26.
    Linden G, Smith B, York J. Amazon.com recommendations: Item-to-item collaborative filtering [J]. Internet Computing, IEEE, 2003, 7(1): 76–80.Google Scholar
  27. 27.
    Peng Tao. Research and achievement of wireless mobile environment image information in recommendation system [D]. Beijing University of Post and Telecommunications, 2010.Google Scholar
  28. 28.
    He Keqin. the research of Recommendation system model and algorithm which is based on label [D]. East China Normal University, 2011.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Liangbin Yang
    • 1
  • Binyang Li
    • 1
  • Xinli Zhou
    • 1
  • Yanmei Kang
    • 1
  1. 1.School of Information Science and TechnologyUniversity of International RelationsBeijingChina

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